Pod Lead — Chemistry(Terminal Bench Science)

About Turing: Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies. Role Overview: We are seeking an experienced Chemistry expert to lead a pod of trainers building realistic, terminal-based scientific tasks for Terminal Bench Science. You will review every task your trainers produce for chemical correctness, reproducibility, safety, and grader reliability, and coach trainers to consistently deliver high-quality, challenging tasks. What you'll do: Review trainer tasks end to end, including task statements, molecular structures, spectra and datasets, force-field parameters, environments, reference solutions, and automated tests. Verify that tasks reflect authentic chemistry research workflows and require genuine multi-step reasoning rather than a single library call or lookup. Check that software versions, force fields, basis sets, functionals, and dependencies are pinned and that results are reproducible. Validate chemical correctness, including units such as Hartree, eV, kcal/mol, and kJ/mol; charge and spin multiplicity; stereochemistry and tautomer handling; method specifications; SCF and geometry convergence; frequency checks for minima and transition states; equilibration; and sampling adequacy. Ensure tolerances reflect method-dependent, numerical, and stochastic variation without accepting chemically incorrect results. Ensure graders verify energies, geometries, thermodynamic quantities, rate constants, spectral assignments, or molecular properties rather than formatting. Ensure tasks cannot be gamed through hard-coded outputs, reading test files, or bypassing the intended computation. Screen tasks for chemical safety and dual-use concerns. Escalate content involving the synthesis or enhancement of hazardous, toxic, or controlled substances. Provide clear, actionable written feedback and track revisions through closure. Onboard and mentor trainers on task design standards, common pitfalls, and grader design. Allocate work across the pod and track throughput and quality metrics. Escalate blockers. Maintain pod documentation covering guidelines, recurring issues, and exemplary tasks. What we're looking for: Ph.D., postdoctoral experience, or equivalent advanced experience in chemistry or a closely related field. Strong programming skills in Python, C/C++, Julia, or Fortran, with Linux and terminal proficiency. Hands-on experience with computational chemistry, molecular simulation, cheminformatics, or chemical data analysis. Proven experience reviewing code, analyses, manuscripts, or research outputs. Experience mentoring or leading small technical or research teams. Excellent written communication skills for providing precise, constructive feedback Nice to have: Familiarity with PySCF, Psi4, ORCA, xTB, CP2K, NWChem, GROMACS, OpenMM, AmberTools, RDKit, Open Babel, ASE, or MDAnalysis. Experience with free-energy methods, reaction mechanism analysis, machine-learned potentials, or spectroscopy simulation. Familiarity with Docker, Git, CI systems, automated testing, and HPC environments. Experience designing benchmarks, programming tasks, or simulation-based evaluations. Offer Details: Commitments Required: At least 4 hours per day and minimum 40 hours per week with overlap of 4 hours with PST. Employment type : Contractor assignment (no medical/paid leave) Duration of contract : 4 week [expected start date is next week]